hushem_40x_deit_small_rms_001_fold3

This model is a fine-tuned version of facebook/deit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 3.1579
• Accuracy: 0.7442

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.001
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.2419	1.0	217	1.3981	0.2791
1.0235	2.0	434	1.3169	0.3953
0.8369	3.0	651	1.0743	0.4884
0.7963	4.0	868	0.6563	0.6977
0.7399	5.0	1085	1.1403	0.4651
0.591	6.0	1302	0.6390	0.7209
0.4772	7.0	1519	0.8818	0.6047
0.4582	8.0	1736	0.8295	0.6744
0.4273	9.0	1953	1.1233	0.4884
0.3402	10.0	2170	0.8028	0.7442
0.3174	11.0	2387	1.2880	0.5581
0.2909	12.0	2604	1.5844	0.6512
0.2204	13.0	2821	1.1940	0.6977
0.2639	14.0	3038	1.0276	0.6279
0.2085	15.0	3255	1.7122	0.6512
0.1551	16.0	3472	1.0876	0.7209
0.2066	17.0	3689	1.4826	0.6279
0.1259	18.0	3906	1.7194	0.6279
0.1381	19.0	4123	1.1881	0.7442
0.0864	20.0	4340	2.4912	0.7209
0.1059	21.0	4557	1.6650	0.6977
0.0958	22.0	4774	1.6843	0.6977
0.0803	23.0	4991	2.0214	0.6279
0.0716	24.0	5208	2.3668	0.6977
0.0335	25.0	5425	1.8384	0.6279
0.0722	26.0	5642	1.9563	0.6744
0.0543	27.0	5859	2.2739	0.6744
0.024	28.0	6076	1.7616	0.6977
0.0588	29.0	6293	1.9807	0.6977
0.0731	30.0	6510	2.0008	0.6279
0.0315	31.0	6727	2.2264	0.7209
0.0084	32.0	6944	2.2231	0.7674
0.0194	33.0	7161	2.3580	0.6977
0.0559	34.0	7378	2.5423	0.7209
0.0002	35.0	7595	2.6899	0.7674
0.0092	36.0	7812	2.7843	0.6744
0.0002	37.0	8029	2.7034	0.7442
0.016	38.0	8246	2.9844	0.7674
0.0006	39.0	8463	1.9924	0.8140
0.006	40.0	8680	2.8801	0.6977
0.0001	41.0	8897	2.7323	0.7674
0.0001	42.0	9114	3.2030	0.6977
0.0002	43.0	9331	3.6553	0.7674
0.0001	44.0	9548	2.9080	0.7209
0.0001	45.0	9765	2.8393	0.7442
0.0	46.0	9982	2.9525	0.7442
0.0	47.0	10199	3.0057	0.7442
0.0	48.0	10416	3.0880	0.7442
0.0	49.0	10633	3.1339	0.7442
0.0	50.0	10850	3.1579	0.7442

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2